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Build Duration Trends

get_build_times
Read-onlyIdempotent

Analyze build duration trends by computing average, minimum, and maximum times to spot performance regressions in Zuul CI.

Instructions

Build duration trends — compute avg/min/max to detect performance regressions.

Returns ALL results (no result filtering). For SUCCESS-only averages, use get_job_durations instead.

Args: tenant: Tenant (default from env) job_name: Job name filter project: Project filter pipeline: Pipeline filter branch: Branch filter limit: Max results, 1-100 (default 20) skip: Pagination offset detail: Include individual build list (default true)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skipNo
limitNo
branchNo
detailNo
tenantNo
projectNo
job_nameNo
pipelineNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint: false. The description adds valuable behavioral context beyond these: it returns ALL results (no result filtering), and the 'detail' parameter controls whether an individual build list is included. This goes beyond what annotations provide, though it doesn't discuss pagination behavior or performance implications.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: a one-sentence purpose, a clear note about result filtering with sibling alternative, then a compact Args list. Every sentence adds value, and the formatting makes key details easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (8 optional parameters) and strong annotations, the description fully covers usage context: what it does, when to use it, key behavioral traits (all results, filters, pagination), and parameter meanings. Output schema is present, so return-value details are not needed in the description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, and the description compensates by listing all 8 parameters with helpful semantics: tenant default comes from env, limit is 'Max results, 1-100 (default 20)', detail includes 'individual build list'. This adds meaning beyond the bare schema titles and defaults, though some parameters (branch, project, pipeline) are only described as 'filter' without deep detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool computes build duration trends (avg/min/max) to detect performance regressions, using a specific verb and resource. It further distinguishes itself from the sibling get_job_durations by noting it returns ALL results with no filtering, making its purpose unique.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use this tool (detect performance regressions) and provides a direct alternative: 'For SUCCESS-only averages, use get_job_durations instead.' Also clarifies that it returns all results (no result filtering), guiding the agent away from inappropriate use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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